Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. An imaging system for collecting an image, the imaging system comprising: a camera module, the camera module having at least one calibration non-uniformity profile associated with a calibration correlated color temperature (CCT); an image signal processor in data communication with the camera module; and a hardware storage device in communication with the image signal processor and having stored thereon a plurality of anchor non-uniformity profiles, each anchor non-uniformity profile being associated with a CCT and at least one of the anchor non-uniformity profiles being associated with the calibration CCT, the hardware storage device having instruction stored thereon, that when executed by the image signal processor, cause the imaging system to: receive a collected image from the camera module, calculate an image CCT of the collected image, and generate a local non-uniformity profile from a delta value between the at least one calibration non-uniformity profile and a first anchor non-uniformity profile of the calibration CCT and a second anchor non-uniformity profile associated with the image CCT, the delta value being a subtraction between the at least one calibration non-uniformity profile and the first anchor non-uniformity profile or the second anchor non-uniformity profile.
Imaging systems for collecting images. The problem addressed is correcting for non-uniformity in images, which can vary with color temperature. The invention describes an imaging system that includes a camera module and an image signal processor. The camera module has a calibration non-uniformity profile linked to a specific correlated color temperature (CCT). The image signal processor is connected to the camera module. A hardware storage device, in communication with the image signal processor, stores multiple anchor non-uniformity profiles, each also associated with a CCT. Crucially, at least one of these anchor profiles matches the calibration CCT. Instructions stored on the hardware storage device, when executed by the image signal processor, direct the system to: receive an image from the camera module, determine the image's CCT, and then create a local non-uniformity profile. This local profile is generated by calculating a delta value. This delta value is derived from the difference between the camera module's calibration non-uniformity profile and either a first anchor non-uniformity profile (which corresponds to the calibration CCT) or a second anchor non-uniformity profile (which corresponds to the image's CCT). The delta value is specifically calculated by subtracting one non-uniformity profile from another.
2. The imaging system of claim 1 , the camera module having a first calibration non-uniformity profile at a first CCT and a second calibration non-uniformity profile at a second CCT.
This invention relates to an imaging system designed to address color accuracy issues in digital cameras, particularly when operating under varying correlated color temperature (CCT) conditions. The system includes a camera module with a sensor and a processor configured to apply calibration profiles to correct color non-uniformities. The camera module has a first calibration non-uniformity profile for a first CCT and a second calibration non-uniformity profile for a second CCT. These profiles compensate for spatial variations in color response across the sensor, ensuring consistent color accuracy regardless of lighting conditions. The processor dynamically selects and applies the appropriate calibration profile based on the detected CCT, improving image quality in environments with mixed or changing light sources. The system may also include additional calibration profiles for other CCT values, allowing for fine-grained adjustments. The invention is particularly useful in applications requiring high color fidelity, such as professional photography, medical imaging, and industrial inspection, where accurate color representation is critical. By mitigating sensor non-uniformities, the system enhances color consistency and reduces the need for post-processing corrections.
3. The imaging system of claim 1 , the plurality of anchor non-uniformity profiles being measured from at least three other camera modules.
The imaging system is designed to improve image quality by correcting non-uniformities in captured images. The system addresses the problem of variations in sensor response, lens distortions, and other optical imperfections that degrade image quality across different camera modules. To solve this, the system uses a plurality of anchor non-uniformity profiles, which are pre-measured from at least three other camera modules. These profiles represent known non-uniformities in the imaging process, such as pixel defects, vignetting, or color inconsistencies. By comparing the captured image data from a target camera module against these anchor profiles, the system can identify and correct corresponding non-uniformities in real-time or during post-processing. The use of multiple anchor profiles ensures robustness, as variations across different camera modules are accounted for. The system may also include calibration techniques to refine the anchor profiles over time, improving accuracy. This approach enhances image consistency and quality across multiple camera modules, particularly in applications requiring high precision, such as medical imaging, surveillance, or industrial inspection.
4. The imaging system of claim 1 , the hardware storage device being part of the image signal processor.
An imaging system captures and processes image data from an imaging sensor, such as a camera, to generate a final image. The system includes a hardware storage device integrated into an image signal processor (ISP) to temporarily store intermediate image data during processing. The ISP performs operations like demosaicing, noise reduction, and color correction on the raw image data before outputting the final image. The hardware storage device ensures efficient data handling by providing fast access to intermediate results, reducing bottlenecks in the processing pipeline. This integration minimizes latency and improves overall system performance by eliminating the need for external memory transfers. The system is particularly useful in high-resolution or real-time imaging applications where processing speed and efficiency are critical. The hardware storage device may be implemented as a dedicated memory buffer or a specialized cache within the ISP, optimized for the specific data types and processing steps involved in image signal processing. This approach enhances image quality and processing throughput while reducing power consumption compared to systems relying on external memory. The system is applicable in digital cameras, smartphones, medical imaging devices, and other applications requiring high-performance image processing.
5. The imaging system of claim 1 , the instructions further causing the imaging system to: apply the local non-uniformity profile to the collected image to create a corrected image.
This invention relates to imaging systems designed to correct non-uniformities in captured images. The problem addressed is the presence of local non-uniformities in images, such as variations in brightness, color, or other artifacts caused by sensor imperfections, lens distortions, or environmental factors. These non-uniformities degrade image quality and accuracy, particularly in applications requiring precise measurements or analysis. The imaging system includes a sensor for capturing an image and a processor executing instructions to process the captured data. The system generates a local non-uniformity profile, which represents spatial variations in the imaging system's response. This profile is derived from calibration data or real-time measurements. The system then applies this profile to the collected image to correct the non-uniformities, producing a corrected image with improved uniformity and accuracy. The correction process may involve adjusting pixel values, interpolating corrections, or applying mathematical transformations to mitigate distortions. The invention is particularly useful in medical imaging, scientific research, industrial inspection, and other fields where image fidelity is critical. By dynamically correcting non-uniformities, the system enhances the reliability and interpretability of the captured images. The method ensures that the corrected image closely matches the true scene, reducing errors in subsequent analysis or decision-making.
6. A method of processing a collected image, the method comprising: receiving a collected image data from a camera module, the camera module having at least one calibration non-uniformity profile associated with the camera module at a calibration correlated color temperature (CCT); calculating an image CCT of the collected image data; determining a delta value between the at least one calibration non-uniformity profile and a first anchor non-uniformity profile of the calibration CCT by subtracting the at least one calibration non-uniformity profile and the first anchor non-uniformity profile of the calibration CCT; generating a local non-uniformity profile from the delta value and a second anchor non-uniformity profile associated with the image CCT; and applying the local non-uniformity profile to the collected image data to create a corrected image data.
This invention relates to image processing techniques for correcting non-uniformities in images captured by camera modules. The problem addressed is the presence of color and brightness inconsistencies in images due to variations in camera sensor performance, which can be influenced by factors such as temperature and lighting conditions. Traditional calibration methods often rely on fixed profiles that do not account for real-time changes in the imaging environment, leading to suboptimal corrections. The method involves receiving image data from a camera module that has at least one calibration non-uniformity profile associated with a specific correlated color temperature (CCT). The image CCT of the collected image is calculated to determine the current lighting conditions. A delta value is computed by subtracting the calibration non-uniformity profile from an anchor profile corresponding to the calibration CCT. This delta value is then used to adjust a second anchor profile associated with the image CCT, generating a local non-uniformity profile tailored to the current conditions. Finally, this local profile is applied to the collected image data to produce a corrected image with improved uniformity. The approach dynamically adapts to varying CCTs, ensuring more accurate corrections compared to static calibration methods.
7. The method of claim 6 , further comprising displaying the corrected image data to a user.
A system and method for image correction and display involves processing image data to enhance visual quality. The method includes receiving image data from an imaging device, such as a camera or sensor, and analyzing the data to detect distortions, noise, or other imperfections. The system applies correction algorithms to adjust the image data, compensating for lens distortions, color inaccuracies, or other artifacts. The corrected image data is then displayed to a user on a display device, such as a monitor or screen. The correction process may involve techniques like dewarping, color calibration, or noise reduction to improve clarity and accuracy. The system ensures that the final displayed image is visually accurate and free from distortions, enhancing user experience in applications like medical imaging, surveillance, or consumer electronics. The method may also include user interface elements to allow manual adjustments or preferences for further customization. The overall goal is to provide a seamless and high-quality image display solution.
8. The method of claim 6 , the image CCT being between CCTs of two neighboring anchor non-uniformity profiles of the plurality of anchor non-uniformity profiles, the local non-uniformity profile being generated from a combination of the two neighboring anchor non-uniformity profiles.
This invention relates to color temperature correction in imaging systems, specifically addressing the challenge of accurately adjusting color temperature (CCT) for images where the CCT falls between predefined anchor points. In imaging systems, non-uniformity profiles are used to correct color inconsistencies across different regions of an image. However, when the CCT of an image lies between two predefined anchor profiles, interpolation is required to generate an appropriate correction profile. The method involves selecting two neighboring anchor non-uniformity profiles from a set of predefined profiles, where the image's CCT falls between the CCTs of these two anchors. A local non-uniformity profile is then generated by combining the two neighboring anchor profiles. This combination may involve interpolation or other blending techniques to produce a profile that accurately corrects the image's color temperature. The resulting profile is applied to the image to achieve uniform color temperature correction across the entire image. This approach ensures smooth and accurate color temperature adjustments, even when the image's CCT does not exactly match any of the predefined anchor profiles. The method is particularly useful in imaging systems where precise color consistency is critical, such as in medical imaging, professional photography, or display calibration.
9. The method of claim 8 , the local non-uniformity profile being generated from a weighted combination of the two neighboring anchor non-uniformity profiles based on a color temperature difference of the image CCT and the CCTs of the two neighboring anchor non-uniformity profiles.
This invention relates to image processing techniques for correcting non-uniformity in images, particularly in scenarios where color temperature variations exist across different regions of an image. The problem addressed is the challenge of accurately correcting non-uniformity in images where neighboring regions have different color temperature characteristics, leading to inconsistencies in the final corrected image. The method involves generating a local non-uniformity profile for a specific region of an image by combining two neighboring anchor non-uniformity profiles. These anchor profiles are predefined and correspond to regions with known color temperature characteristics. The combination is weighted based on the color temperature difference between the image's color temperature (CCT) and the CCTs of the two neighboring anchor profiles. This ensures that the correction applied to the local region is more accurate and better adapted to the specific color temperature conditions of that region. The process begins by identifying the two nearest anchor non-uniformity profiles that are relevant to the local region being processed. The color temperature of the image (CCT) is then compared to the CCTs of these anchor profiles. A weighted combination of the two anchor profiles is computed, where the weights are determined by the relative color temperature differences. This weighted combination produces a local non-uniformity profile that is tailored to the specific color temperature conditions of the local region, improving the overall uniformity correction in the final image. The method is particularly useful in applications where precise color consistency is required, such as in medical imaging, professional photography, or high-end display technologies.
10. The method of claim 8 , further comprising storing the local non-uniformity profile locally on a hardware storage device.
A system and method for managing non-uniformity in imaging devices, particularly in thermal imaging or other sensor arrays, addresses the problem of variations in sensor response across an array due to manufacturing tolerances or environmental factors. These variations can lead to inaccurate or distorted images, reducing the reliability of the imaging system. The invention involves generating a local non-uniformity profile that compensates for these variations by characterizing the response differences of individual sensors or sensor groups. This profile is then applied to correct the output data from the imaging device, improving uniformity and accuracy. The method includes capturing calibration data from the imaging device under controlled conditions, analyzing the data to identify non-uniformities, and generating a correction profile. The profile is stored locally on a hardware storage device, such as a memory chip or solid-state drive, to ensure quick access and real-time correction during operation. This local storage allows the imaging device to apply corrections without relying on external systems, enhancing efficiency and reliability. The invention may also include periodic recalibration to account for changes in sensor performance over time. The stored profile can be updated or replaced as needed to maintain optimal performance. This approach is particularly useful in applications requiring high precision, such as medical imaging, industrial inspection, or scientific research.
11. The method of claim 8 , further comprising storing the local non-uniformity profile remotely and comparing the local non-uniformity profile against other local non-uniformity profiles created from a population used to create the first anchor non-uniformity profile.
This invention relates to a method for analyzing and managing non-uniformity profiles in imaging systems, particularly for improving image quality by correcting spatial variations in sensor response. The method addresses the problem of inconsistent image quality due to sensor non-uniformities, which can arise from manufacturing defects, environmental factors, or sensor aging. By generating and comparing non-uniformity profiles, the system enables precise calibration and correction of these variations. The method involves creating a local non-uniformity profile for an imaging device, which captures spatial variations in sensor response. This profile is then stored remotely and compared against other local non-uniformity profiles derived from a population of devices used to generate a reference or anchor non-uniformity profile. The comparison helps identify deviations in individual devices relative to the broader population, allowing for targeted corrections. The system may also adjust the anchor profile based on new data, ensuring continuous improvement in calibration accuracy. This approach enhances image consistency across multiple devices by leveraging collective data, reducing the need for frequent individual recalibrations. The method is particularly useful in applications requiring high-precision imaging, such as medical diagnostics, industrial inspection, and scientific research.
12. The method of claim 8 , calculating the image CCT including using an auto white balance operation.
A method for determining the correlated color temperature (CCT) of an image involves analyzing the image data to estimate the lighting conditions under which the image was captured. The process includes performing an auto white balance (AWB) operation to adjust the color balance of the image, which helps in accurately assessing the color temperature. The AWB operation compensates for the color cast introduced by the lighting source, ensuring that the calculated CCT reflects the true color temperature of the illumination. This method is particularly useful in digital imaging systems where accurate color temperature estimation is required for applications such as image enhancement, color correction, and lighting analysis. By incorporating the AWB operation, the method improves the reliability of the CCT calculation, making it more robust against variations in lighting conditions and camera settings. The technique can be applied in various imaging devices, including digital cameras, smartphones, and surveillance systems, to ensure consistent and accurate color temperature measurements.
13. A method of processing a collected image, the method comprising: receiving a collected image data from a camera module, the camera module having at least one calibration non-uniformity profile associated with the camera module at a calibration correlated color temperature (CCT); calculating an image CCT of the collected image data, the image CCT being between CCTs of two neighboring anchor non-uniformity profiles associated with the calibration CCT; determining a delta value between the at least one calibration non-uniformity profile and a calculated anchor non-uniformity profile, the calculated anchor non-uniformity profile being located between the two neighboring anchor non-uniformity profiles and based on a color temperature difference between the two neighboring anchor non-uniformity profiles; generating a local non-uniformity profile from the delta value and the calculated anchor non-uniformity profile; applying the local non-uniformity profile to the collected image data to create a corrected image data; and displaying the corrected image data to a user.
This method relates to image processing for correcting color non-uniformities in images captured by a camera module. The problem addressed is the presence of color inconsistencies in images due to variations in the camera's response across different color temperatures, which can degrade image quality. The solution involves dynamically adjusting image data based on calibration profiles to achieve uniform color representation. The method begins by receiving image data from a camera module, which has at least one calibration non-uniformity profile associated with a specific correlated color temperature (CCT). The image CCT of the collected data is calculated, and if it falls between two neighboring anchor non-uniformity profiles, a delta value is determined between the calibration profile and a calculated anchor profile. The calculated anchor profile is derived from the two neighboring profiles based on their color temperature differences. A local non-uniformity profile is then generated using the delta value and the calculated anchor profile. This profile is applied to the collected image data to correct color inconsistencies, resulting in corrected image data that is displayed to the user. The approach ensures accurate color representation by interpolating between predefined calibration profiles, adapting to varying lighting conditions.
14. The method of claim 13 , wherein the calculated anchor non-uniformity profile is determined from a weighted combination of the two neighboring anchor non-uniformity profiles.
This invention relates to a method for determining a non-uniformity profile in imaging systems, particularly for correcting distortions in captured images. The problem addressed is the presence of non-uniformities in imaging sensors, such as variations in pixel sensitivity or optical distortions, which degrade image quality. The method involves calculating an anchor non-uniformity profile for a target region of an image sensor by combining the non-uniformity profiles of two neighboring regions. The neighboring regions are selected based on their proximity to the target region, and their profiles are weighted according to their distance or other relevant factors. This approach allows for accurate interpolation of non-uniformity corrections without requiring direct measurements in the target region, improving efficiency and reducing computational overhead. The method is particularly useful in applications where direct calibration of every sensor region is impractical, such as in large-scale imaging systems or real-time processing environments. By leveraging neighboring profiles, the technique ensures smooth and consistent corrections across the entire sensor, enhancing overall image uniformity and quality.
15. The method of claim 13 , further comprising storing the local non-uniformity profile locally on a hardware storage device.
A system and method for managing non-uniformity in imaging devices, particularly in thermal imaging or sensor arrays, addresses the problem of inconsistent performance across different pixels or sensors due to manufacturing variations or environmental factors. The invention involves generating a local non-uniformity profile that compensates for these inconsistencies by measuring and correcting deviations in sensor outputs. This profile is dynamically adjusted based on real-time data to ensure accurate and consistent imaging. The method further includes storing this profile locally on a hardware storage device, such as a memory chip or solid-state drive, to enable quick access and efficient correction during operation. By maintaining the profile locally, the system avoids delays associated with remote storage or processing, improving real-time performance. The stored profile can be periodically updated to account for changes in sensor behavior over time, ensuring long-term accuracy. This approach is particularly useful in applications requiring high precision, such as medical imaging, industrial inspections, or autonomous navigation, where sensor reliability is critical. The local storage ensures that corrections are applied without latency, enhancing overall system efficiency and accuracy.
16. The method of claim 13 , further comprising storing the local non-uniformity profile remotely and comparing the local non-uniformity profile against other local non-uniformity profiles created from a population used to create the first anchor non-uniformity profile.
This invention relates to a method for analyzing and managing non-uniformity profiles in imaging systems, particularly for identifying and correcting variations in image quality across different devices or populations. The method involves generating a local non-uniformity profile for an imaging device by capturing multiple images under controlled conditions and analyzing pixel intensity variations. This local profile is then compared to a reference or anchor non-uniformity profile, which is derived from a broader population of imaging devices. The comparison helps identify deviations specific to the local device, allowing for targeted corrections to improve image uniformity. Additionally, the local non-uniformity profile is stored remotely and compared against other local profiles from the same population to detect trends or anomalies. This enables large-scale analysis of imaging performance across multiple devices, facilitating quality control and calibration improvements. The method supports real-time adjustments and long-term monitoring, ensuring consistent image quality in applications such as medical imaging, surveillance, or industrial inspection.
17. The method of claim 13 , calculating the image CCT including using an auto white balance operation.
A method for determining the correlated color temperature (CCT) of an image involves analyzing the image data to estimate the CCT, which is a measure of the color appearance of light sources in the image. The method addresses the challenge of accurately determining CCT in varying lighting conditions, which is crucial for applications such as digital imaging, color correction, and lighting analysis. The process includes capturing an image using an imaging device, such as a camera, and processing the image data to extract color information. The method then applies an auto white balance (AWB) operation to adjust the color balance of the image, compensating for the color cast introduced by the lighting conditions. The AWB operation helps neutralize the color temperature, allowing for a more accurate CCT calculation. The method further involves analyzing the adjusted image data to determine the CCT, which can be used for applications like color grading, lighting control, and display calibration. The technique ensures that the CCT is derived from a color-balanced image, improving the reliability of the measurement in diverse lighting environments. This approach is particularly useful in digital photography, video processing, and automated lighting systems where precise color temperature assessment is required.
18. The method of claim 13 , wherein calculating the delta value includes determining a difference at each pixel of the camera model between the calibration non-uniformity profile of the camera module at the calibration CCT and a first anchor non-uniformity profile at the calibration CCT.
This invention relates to camera calibration techniques for correcting non-uniformity in image sensors, particularly in response to changes in correlated color temperature (CCT). The problem addressed is the variation in pixel response across an image sensor due to manufacturing imperfections and environmental factors, which can degrade image quality. The solution involves generating a calibration non-uniformity profile at a known CCT and comparing it to a first anchor non-uniformity profile at the same CCT to compute a delta value. This delta value represents the difference in pixel response between the calibration profile and the anchor profile, allowing for adjustments to correct non-uniformity. The method ensures accurate color and brightness correction across the sensor, improving image consistency. The approach is particularly useful in applications requiring high-precision imaging, such as medical, scientific, or industrial cameras, where uniformity is critical. By quantifying the differences at each pixel, the system can dynamically adjust for variations, enhancing overall image quality. The technique may be part of a broader calibration process that includes multiple anchor profiles to account for different CCT conditions, ensuring robustness across varying lighting environments.
19. The method of claim 13 , wherein calculating the delta value includes determining a difference at each domain of the camera model between the calibration non-uniformity profile of the camera module at the calibration CCT and a first anchor non-uniformity profile at the calibration CCT.
This invention relates to camera calibration techniques for correcting non-uniformity in image sensors, particularly in response to changes in correlated color temperature (CCT). The problem addressed is the variation in sensor response across different domains (e.g., spatial, spectral, or temporal) due to CCT shifts, which can degrade image quality. The method involves generating a calibration non-uniformity profile for a camera module at a known CCT and comparing it to a first anchor non-uniformity profile at the same CCT. The difference between these profiles at each domain of the camera model is calculated to produce a delta value. This delta value quantifies the non-uniformity correction needed to maintain consistent image quality across varying lighting conditions. The approach allows for dynamic adjustments based on real-time CCT measurements, ensuring accurate color and brightness uniformity regardless of environmental changes. The method is particularly useful in applications requiring high-precision imaging, such as medical, scientific, or industrial cameras, where sensor performance must remain stable under different lighting scenarios. By leveraging domain-specific differences, the technique provides a more granular and adaptive correction mechanism compared to traditional uniform calibration methods.
20. The method of claim 13 , wherein calculating the delta value includes calculating a plurality of delta values at a plurality of calibration CCTs and aggregating the calculated plurality of delta values into an average delta value.
This invention relates to a method for calibrating color temperature in lighting systems, particularly for improving color consistency across different light sources. The problem addressed is the variation in color output of light sources due to manufacturing tolerances, aging, or environmental factors, which can lead to perceptible color differences even when the correlated color temperature (CCT) is nominally the same. The method involves calculating a delta value representing the color difference between a reference light source and a target light source at a specific CCT. To enhance accuracy, the method calculates multiple delta values at multiple calibration CCTs and then aggregates these values into an average delta value. This averaging process helps mitigate errors caused by variations at individual CCTs, providing a more reliable calibration. The method may also include adjusting the target light source's output based on the calculated delta value to match the reference light source's color characteristics. The calibration process ensures that multiple light sources, even from different batches or manufacturers, can be tuned to produce consistent color output. This is particularly useful in applications requiring uniform lighting, such as commercial displays, automotive lighting, or large-scale installations where color consistency is critical. The method may be implemented in a lighting control system that dynamically adjusts light source parameters to maintain color accuracy over time.
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December 8, 2020
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